Method for improving images applicable to fingerprint images

ABSTRACT

The invention relates to a method for processing an image comprising a set of pixels, each pixel being associated with a grey level, the method comprising a step of segmentation of the image to generate a modified image only containing regions of the image having an alternation of bright zones and dark zones at a frequency greater than a minimum frequency, said step of segmentation comprising:
         the allocation, to each pixel of the image, of a frequency response level, corresponding to a frequency of alternations of bright zones and dark zones in the vicinity of the pixel,   the definition of regions of the image by grouping together neighbouring pixels of same frequency response level,   the determination of a threshold frequency response level, and   the generation of an image only comprising regions of which the pixels have a frequency response level greater than or equal to the threshold frequency response level.

FIELD OF THE INVENTION

The invention relates to a method for processing a finger image acquiredby a fingerprint sensor of direct view type and comprising at least onefingerprint in order to implement an authentication or identification ofan individual by comparison of fingerprints. The invention applies inparticular to the processing of fingerprint images acquired by sensorsof thin film transistor type.

PRIOR ART

Novel types of sensors are currently being developed for the acquisitionof fingerprint images, based notably on the direct view of the finger.This is the case for example of sensors based on TFT (Thin FilmTransistor) technology.

These sensors may be less bulky and quicker to use than sensorsconventionally used until now, which are sensors based on the principleof frustrated total reflection of light on an interface on which a userlays down his/her finger.

Indeed, these sensors may for example take a series of images of thehand of an individual approaching a contact surface of the sensor, andexploit images of the fingers even in the absence of contact with thecontact surface, or in the absence of significant pressure exerted bythe fingers on the contact surface.

On the other hand, direct view based sensors produce a fingerprint imagein which the contrast is in general much lower than images obtained bysensors based on the principle of frustrated total reflection. Inaddition, parasitic zones may exist on the images, such as for examplecast shadows in the case where an image has been acquired in the absenceof contact of the fingers on the contact surface of the sensor.

Hence, the fingerprint images obtained by direct view based sensors arenot at this stage directly exploitable by the algorithms implemented insystems for automatic identification or authentication fromfingerprints.

In order to make these images compatible, that is to say to ensure thata fingerprint acquired with a sensor with direct lighting can berecognised using the same algorithms as prints acquired withconventional technologies based on frustrated total reflection, it isnecessary to propose appropriate processing.

This processing has to take into account the great variability of theimages obtained with direct view based sensors. For example, shadowscast on the images may have very variable sizes and positions dependingon the number and the position of the light sources illuminating thefinger, and the position of the hand during the acquisition.

In addition, since it is a processing making it possible to implement anauthentication or identification of an individual by comparison offingerprints on a finger image acquired by a fingerprint sensor ofdirect view type, it is necessary that the processing method is veryrapid. Indeed, a fingerprint sensor of direct view type generallyacquires more than 10 images per second, for example 15 or 20 images persecond, which moreover is of large size/resolution (for example1600×1600 pixels) and the processing of these images must be immediate.

Certain processing methods are suited for processing images of latentfingerprints, that is to say images of the residual trace resulting fromthe apposition of a finger on a support. It is notably these latentfingerprints that are revealed in crime scenes and analysed by forensicteams. The processing of latent fingerprints does not have a rapidityconstraint. Thus, the document “Automatic Latent FingerprintSegmentation based on Orientation and Frequency Features”, by Revathy etal., International Conference on Communication and Signal Processing,3-5 Apr. 2014, pages 1192-1196, describes a method for processing latentfingerprints implementing an automatic segmentation of images, based onthe characteristics of orientation and frequency, comprising notably theimplementation of a discrete Fourier transform. The proposed processingis very heavy, and very long. It may take more than a half-second for animage NIST SD 27 of size 800×800. Such a duration is not possible forprocessing a finger image acquired by a fingerprint sensor of directview type, which takes more than 10 images per second.

In addition, the processings proposed for latent fingerprints aim toextract all the ridge information of the image. Yet, an image acquiredby a fingerprint sensor of direct view type frequently has, at thesurface of the sensor, traces outside of the finger resulting frompreceding finger contacts (which are thus latent fingerprints). Theprocessing implemented must be able to distinguish between these tracesand the fingerprints of the finger presented to the sensor.

DESCRIPTION OF THE INVENTION

The aim of the invention is to overcome the aforementioned problems, byproposing an image processing method that makes it possible to adapt afingerprint image acquired with a direct view sensor, for example of TFTtype, such that the image can be exploited in an automatic fingerprintidentification or authentication system.

Another aim of the invention is to propose a processing that issufficiently rapid to be able to be implemented in real time during theacquisition of images by a sensor.

Another aim of the invention is to propose a processing that makes itpossible to generate a fingerprint image exploitable by anidentification or authentication system that is generated from a singleinitial image, and not synthesised from several takes, in order to ruleout risks of errors linked to a movement between two takes.

In this respect, the subject matter of the invention is a method forprocessing a finger image acquired by a fingerprint sensor of directview type and comprising at least one fingerprint in order to implementan authentication or identification of an individual by comparison offingerprints, said image comprising a set of pixels, each pixel beingassociated with a grey level, the method comprising a step ofsegmentation of the image to generate a modified image only containingregions of the image having an alternation of bright zones and darkzones at a frequency greater than a minimum frequency, said step ofsegmentation comprising:

-   -   the allocation, to each pixel of the image, of a frequency        response level, corresponding to a frequency of alternations of        bright zones and dark zones in the vicinity of the pixel, by        evaluating the variabilities of the grey levels of the pixels        contained in a window of determined size positioned around each        pixel,    -   the definition of regions of the image by grouping together        neighbouring pixels of same frequency response level,    -   the determination of a threshold frequency response level,    -   selection, among the regions of the image defined previously by        grouping together neighbouring pixels of same frequency response        level, of regions of which the pixels have a frequency response        level greater than or equal to the threshold frequency response        level, and    -   the generation of a modified image only comprising, among the        regions of the image defined previously by grouping together        neighbouring pixels of same frequency response level, regions of        which the pixels have a frequency response level greater than or        equal to the threshold frequency response level.

Advantageously, but optionally, the method according to the inventionmay further comprise at least one of the following characteristics:

-   -   the allocation, to a pixel, of a frequency response level by        evaluating the variabilities of the grey levels of the pixels        contained in a window of determined size positioned around each        pixel comprising the determination of a gradient of grey levels,        the frequency response level allocated to said pixel being based        on said gradient of grey levels;    -   the regions of the image defined by grouping together        neighbouring pixels of same frequency response level are        advantageously structured, to determine the threshold frequency        response level, into a topological tree or a connected component        tree, defining a parent relationship between said regions of the        image;    -   the regions of the image are advantageously structured according        to a parent-daughter relationship defined as follows, for each        region i of the image:        -   initially all the neighbouring regions of the region i on            the image are considered as potential parents,        -   if the number of potential parents is equal to 1, the            neighbour j is assigned as parent to the region i, and the            region i is removed from the list of potential parents of            the region j,        -   if not, each parent region of the region i is selected, from            the list of potential parents having a lower frequency            response level than that of the region i, as that having the            level the closest to that of the region i, and the region i            is removed from the list of potential parents of its parent            region, and        -   if all the potential parents of the region i have a            frequency response level higher than that of the region i,            each parent region of the region i is selected as that            having the frequency response level the closest to that of            the region i, and the region i is removed from the list of            potential parents of its parent region;    -   the determination of the threshold frequency response level may        comprise the implementation of the following steps:        -   for a set frequency response level N, definition of a set of            macro-regions designated level N, such that each            macro-region of level N comprises a parent region of pixels            of frequency response level less than or equal to N and the            set of daughter regions of this region,        -   for each value of frequency response level from an initial            value of frequency response level in the image, calculation            of the relative variation in surface of the macro-regions of            a level N_(n) compared to the macro-regions of the preceding            level N_(n-1), and        -   the minimum frequency response level is determined as the            level N_(i) for which the relative variation in surface of            the macro-regions of level N_(i) compared to the preceding            level N_(i-1) is minimal;    -   the step of segmentation comprises, before the definition of the        regions of the image by grouping together neighbouring pixels of        same frequency response level, the implementation of the steps        of:        -   morphological erosion then dilation of the image as a            function of the values of grey level of the pixels,        -   generation of a differential image by subtraction, from the            initial image, of the image having undergone morphological            erosion and dilation,        -   application, to the differential image, of a median filter            on the value of the frequency response level,            and in which the definition of the regions of the image by            grouping together neighbouring pixels of same frequency            response level is implemented on the image resulting from            the application of the median filter on the differential            image;    -   the method may further comprise, after the step of segmentation,        a step of enhancement of the grey levels associated with the        pixels of the modified image, the degree of enhancement of the        pixels of a region being a function of the frequency response        level of the pixels of the region;    -   advantageously, the higher the frequency response level of the        pixels of a region of the modified image, the greater the degree        of enhancement of grey levels associated with the pixels of the        region.

The subject matter of the invention is also a computer programmeproduct, comprising code instructions for the implementation of themethod according to the preceding description, when it is executed by aprocessor. Preferably, the computer programme product is a computerreadable support comprising a memory storing code instructions for theimplementation of the method according to the preceding description,when it is executed by a computer.

The subject matter of the invention is also an image processing system,comprising a processing unit comprising processing means configured toimplement the method according to the preceding description, saidprocessing means comprising at least one processor and a memory.

Advantageously, but optionally, the image processing system furthercomprises an image acquisition means, said image acquisition means beinga fingerprint sensor of thin film transistor type.

The proposed method comprises a particular step of segmentation, whichmakes it possible to only conserve, of the initial image, zones of theimage having a high frequency of variations between bright zones anddark zones, which corresponds, for a fingerprint image, to theexploitable part for the identification or the authenticationcorresponding to the ridges and valleys of the fingerprint.

In particular, uniformly bright zones (zones of the image where there isno finger) and uniformly dark zones (cast shadows) are eliminated fromthe image.

The formation of zones is carried out by the implementation of analgorithm of “rising water” type, which makes it possible to conserverelated regions of images and to avoid the presence of holes in thezones corresponding to fingerprints.

The method may include an enhancement of the grey levels of the pixelsas a function of the frequency of variations between bright zones anddark zones: in other words, the more the zone of the image correspondsto an exploitable zone of ridges and valleys, the more the contrast ofthis zone is increased.

Moreover, the proposed method may be implemented in real time during theacquisition of the image because the process of segmentation onlyrequires scanning the image a single time to define a set of regions,then processing the regions by blocks, this step requiring only treatinga number of regions much lower than the number of pixels of the image.

DESCRIPTION OF THE FIGURES

Other characteristics, aims and advantages of the present invention willbecome clearer on reading the detailed description that follows, withregard to the appended figures, given as non-limiting examples, and inwhich:

FIG. 1 represents the main steps of an image processing method.

FIG. 2 schematically represents an image processing system.

FIGS. 3a to 3c schematically represent the implementation of thedifferent steps of the method on an example of image.

DETAILED DESCRIPTION OF AT LEAST ONE EMBODIMENT OF THE INVENTION

With reference to FIG. 1, the main steps of an image processing methodare represented. The processed image comprises a set of pixels, eachpixel being associated with a grey level, conventionally comprisedbetween 0 and 255.

The processed image is advantageously an image of one or more fingers,on the palm side of the hand, and representing the end of the finger(s)on which are found the fingerprints, or the palm itself of the hand.More advantageously, the processed image is an image acquired from afingerprint sensor of direct view type, such as a sensor based on thinfilm transistor (TFT) technology. Reference could be made for example tothe documents US 20020054394 or US 20020000915. Such an image thusgenerally has mainly white pixels, that is to say with a grey level at255, and with dark zones corresponding to shadows and to prints havingpixels with grey levels close to 0.

As schematically represented in FIG. 2, the image processing method isimplemented by an image processing system 1 including a processing unit10 such as for example a computer. The processing unit 10 comprisesprocessing means 11 such as for example a processor. The imageprocessing may be implemented by an appropriate computer algorithm. Theprocessing means 11 are then suited to executing code instructionsmaking it possible to implement the image processing algorithm.

The image processing system 1 also advantageously comprises an imagesensor 20, suited to communicating with the processing unit 10 totransmit, to it, the images acquired. Advantageously, the image sensoris a direct view fingerprint sensor, for example of thin film transistortype.

The image sensor 20 may be remote from the processing unit, andconnected thereto by a wireless connection, for example of WiFi type,etc.

The processing unit 10 includes a memory 12 and a communicationinterface 13 with the image sensor 20. In an alternative embodiment, theimage processing system may also comprise an image database (notrepresented), from which the processing unit may recover images toprocess, these images having been obtained by an image sensor.

FIG. 3a shows an example of image acquired from a direct viewfingerprint sensor. As may be noted, this image is not exploitable, asis, to carry out an identification or authentication processing on thefingerprints, for example by extraction and comparison of the minutiae.Indeed, it includes shadows (around fingers) and finger zones extendingbeyond the fingerprints and lacking interest for an identification orauthentication processing.

Returning to FIG. 1, the image processing method comprises a first step100 of segmentation of the image. This step of segmentation is designedso as to only conserve, of the initial image, zones having a highfrequency of alternation between bright zones and dark zones. This makesit possible, in the case where the processed image is an image of thetip of the fingers bearing the fingerprints, to only conserve the zoneuseful for the fingerprints themselves.

To do so, the step 100 of segmentation comprises a first step 110 ofallocation, to each pixel, of a frequency response level, whichcorresponds to a frequency of alternations between bright zones and darkzones in the vicinity of the pixel.

The level may be determined by positioning around each pixel a window ofdetermined size, and by evaluating the variabilities of the grey levelsof the pixels contained in the window. The window may for example be asquare with sides of the order of 10 to 20 pixels. The evaluation of thevariabilities of the grey levels of the pixels contained in the windowmay comprise, to allocate a frequency response level to a pixel, thedetermination of a gradient of grey levels, the frequency response levelallocated to said pixel being based on said gradient of grey levels.

The gradient of grey levels corresponds to the difference between thegrey level of the pixel and the grey levels of the pixels in the window.More precisely, by noting (z) the pixels contained in the windowsurrounding the pixel (i), the gradient of grey levels of the pixel (i)is calculated by the difference between the grey level of the pixel (i)and the maximum grey level among the minimum grey levels of the pixels(y) contained in a window surrounding each pixel (z) of the windowsurrounding the pixel (i). Preferably, the gradient is calculated inabsolute value.

In other words, by designating Y(i) the set of pixels (z) contained inthe window surrounding the pixel (i), Y(z) the set of pixels (y)contained in the window surrounding each pixel (z), and pixel level thegrey level of a pixel, the frequency response level “response (i)” ofthe pixel (i) may be calculated as follows:

response(i)=abs[level pixel(i)−max_(zεY(i))(min_(yeY(z))level pixel(y))]

Where “abs” designates the absolute value function. It should be notedthat the windows are here taken of same dimension in the maximum andminimum function, but they could be of different dimensions.

From this step, the values processed in each pixel are thus no longergrey levels but frequency response levels. The terminology “pixel” isconserved.

Then the step 100 of segmentation advantageously comprises a step ofmorphological opening of the image, which comprises a step 121 ofmorphological erosion followed by a step 122 of morphological dilationof the image.

Morphological erosion is a processing which consists in assigning to apixel the value of the lowest frequency response level of a window ofpixels incorporating it. For example the window of pixels may be asquare window, for example with sides of the order of 10 pixels, orless, such as sides of 3 pixels.

Morphological dilation is a processing which consists in assigning to apixel the value of the highest frequency response level of a window ofpixels incorporating it. Here again the window of pixels may for examplebe a square window, for example with sides of the order of 10 pixels, orless, such as sides of 3 pixels.

These two steps make it possible to average out the image of which thehigh frequencies of alternations between bright zones and dark zones areeliminated.

The step of morphological closing is followed by a step 130 ofgeneration of a differential image, which is obtained by subtractingfrom the initial image the averaged image resulting from the processingof steps 121 and 122. Thus, the differential image now only includes thezones of interest in the image, that is to say the zones having a highfrequency of alternation between bright zones and dark zones, as may beseen in FIG. 3 b.

Then, the step 100 of segmentation comprises a step 140 of applicationof a median filter to the differential image obtained at the end of thestep 130, the median filter being implemented as a function of thevalues of frequency response level of the pixels.

The segmentation 100 then comprises a step 150 of definition, in theimage, of regions grouping together neighbouring pixels of samefrequency response level. These regions are advantageously structuredinto a topological tree, or a connected components tree. The treedefines a parent-daughter relationship between the regions of the imagedetermined by taking into account both the spatial organisation of theregions defined by grouping together neighbouring pixels of samefrequency response level and the frequency response levels of pixelscontained in these regions. More precisely, it is thus a morphologicaltree of shapes.

To do so, the regions are defined and structured as follows.

A region is firstly defined by the set of neighbouring pixels of samefrequency response level. For each region, a list of neighbouringregions is defined, that is to say regions having pixels in contact withthe pixels of the region considered.

Then a parent relationship between regions or, put another way, adaughter region—parent region relationship, is defined as follows:

-   -   For a region i designated daughter region, initially all the        neighbouring regions of the region i are considered as potential        parents.    -   If the number of potential parents is equal to 1, that is to say        that the region i only has a single neighbour j, the neighbour j        is assigned as parent to the region i, and the region i is        removed from the list of potential parents of the region j.    -   if not, one selects from the list of potential parents having a        frequency response level lower than that of the region i that or        those having the level the closest to that of the region i.        There may thus be several parents for a same region i, which are        then not neighbouring. When the region j is assigned as parent        to the region i, the region i is removed from the list of        potential parents of the region j.    -   If all the potential parents have a frequency response level        higher than that of the region i, one determines as parent for        the region i the region(s) having the frequency response level        the closest to that of the region i (the level then being        greater than that of the region i). When the region j is        assigned as parent to the region i, the region i is removed from        the list of potential parents of the region j.

The segmentation is then going to comprise a selection of the regionsdefined above, which have a frequency response level greater than adetermined threshold level.

However, if by seeking to only conserve the zones of interestcorresponding to fingerprints, the threshold is set at a too high value,there exists a risk of only recovering from the initial image divided upregions, potentially including holes which, although corresponding tozones of lowest frequency responses, may be relevant for theexploitation of prints.

To avoid this phenomenon, the step 100 of segmentation comprises a step160 of determination of the frequency response level thresholdcomprising a first sub-step 161 of definition of macro-regions from theregions and parent-daughter relationships defined above between theregions.

Macro-regions are defined for a set value of level of response N. Forthis set value N, a macro-region includes a region of frequency responselevel lower than or equal to N and all the daughter regions of thisregion.

The definition of such a macro-region is carried out by determining, foreach region of the image, the parent region having the highest frequencyresponse level which is less than the level N. Then, from this parent,the macro-region is defined as grouping together all the daughterregions of this parent.

Given the construction of the parent-daughter relationship definedabove, the daughter regions do not necessarily have a higher frequencyresponse level than the parent region. In certain cases, there exists,for a parent region, daughter regions that are isolated but having alower level (for example the case in which the daughter region only hasone potential parent). As a result of this construction, the fact ofintegrating all the daughter regions in the macro-region makes itpossible to avoid the appearance, in the macro-regions, of empty zonescorresponding to zones of the image globally darker or brighter andwithout alternations of bright and dark zones.

Each macro-region thus comprises a region of pixels of frequencyresponse level less than or equal to N, and a set of neighbouringregions defined as daughter regions by the definition given above.

The definition of a macro-region thus varies as a function of thefrequency response level N. Consequently, the surface of the imageoccupied by the macro-region also varies. This appears in FIG. 3c ,which represents macro-regions defined for several frequency responselevels.

In particular, the higher the level N and the higher the number ofdaughter regions in a macro-region, thus the greater the surface of themacro-region.

The image segmented at the end of the step 100 includes themacro-regions thus defined, for a particular value of frequency responselevel N. To determine this value, the step 160 comprises an incrementalprocess 162 comprising incrementing the value of the frequency responselevel from an initial level, and advantageously up to the highest valueof level in the image. For each increment of the value of the frequencyresponse level, the surface of the image covered by the set ofmacro-regions of the corresponding level is measured, and the relativevariation in the surface occupied by said macro-regions compared to thepreceding level is calculated.

By noting Surf(N_(n)) the surface occupied by the macro-regions offrequency response level N_(n), one calculates:R=(Surf(N_(n))−Surf(N_(n-1)))/Surf(N_(n-1)).

The threshold frequency response level N_(S) determined for thesegmentation of the image as being the level for which the relativevariation in the surface occupied by the macro-regions between one leveland the preceding level is minimal, that is to say when R is the lowest,which corresponds to the level for which the surface of themacro-regions is the most stable.

Once the threshold level has been determined, the step 100 ofsegmentation comprises a step 170 of generation of the segmented image,in which are conserved, of the image resulting from step 140, only themacro-regions of level N_(S). In addition, the overlap zones of themacro-regions are eliminated: when two macro-regions have pixels incommon, the definition of the parent-daughter relationship implies thata macro-region is necessarily incorporated in the other. Then theoverlap zones of macro-regions are eliminated by eliminating themacro-regions incorporated in larger macro-regions.

Then the image processing method comprises a second step 200 ofenhancement of the image obtained at the end of step 100. Enhancement isan operation consisting in modifying the values of grey levels of thepixels of the image to improve the contrast. For example, if in an imagethe pixels have grey levels comprised between 100 and 200, theenhancement consists in allocating to pixels having a grey level at 200a new level at 255, to those having a grey level at 100 a new level at0, and distributing the values of the other pixels between 0 and 255according to a particular law.

To be specific, in step 200, the law of re-assignation of the value ofthe grey level of a pixel is chosen so as to be a function of the valueof the frequency response level of this pixel. In other words, thehigher the frequency response level of a pixel, the higher theenhancement, that is to say the greater the contrast for the pixelsconsidered.

An example of law of re-assignation of the value of the grey level ofthe pixel is the following law:

${g(x)} = {{\left( {1 - a} \right)*\min} + {\left( {x - \min} \right)*\frac{\left( {{Gmax} - {Gmin}} \right)}{\left( {\max - \min} \right)}}}$

With:

(G max−G min)=(1−a)*(max−min)+a*255

Where “a” is the frequency response level of the pixel x, normalised tobe brought between 0 and 1. With a low value of “a”, corresponding to alow frequency response level, the value of the grey level of the pixelwill be almost not modified, and with a high value, the value of thegrey level of the pixel will be more modified.

This makes it possible to improve contrast in the zones of the imagecorresponding to fingerprints, and which are thus the richest ininformation for a future exploitation by an identification orauthentication system.

The image obtained at the end of the processing method is thusexploitable to implement an authentication or identification of anindividual by comparison of fingerprints. Preferably, the method is abiometric method comprising a step of authentication or identificationof an individual by comparison of fingerprints on the basis of themodified image.

1. Method for processing a finger image acquired by a fingerprint sensorof direct view type and comprising at least one fingerprint in order toimplement an authentication or identification of an individual bycomparison of fingerprints, said image comprising a set of pixels, eachpixel being associated with a grey level, the method comprising a stepof segmentation of the image to generate a modified image onlycontaining regions of the image having an alternation of bright zonesand dark zones at a frequency greater than a minimum frequency, saidstep of segmentation comprising: the allocation, to each pixel of theimage, of a frequency response level, corresponding to a frequency ofalternations of bright zones and dark zones in the vicinity of thepixel, by evaluating the variabilities of the grey levels of the pixelscontained in a window of determined size positioned around each pixel,the definition of regions of the image by grouping together neighbouringpixels of same frequency response level previously allocated to eachpixel, the determination of a threshold frequency response level,selection, from the regions of the image defined previously by groupingtogether neighbouring pixels of same frequency response level, ofregions of which the pixels have a frequency response level greater thanor equal to the threshold frequency response level, and the generationof a modified image only comprising, among the regions of the imagedefined previously by grouping together neighbouring pixels of samefrequency response level, regions of which the pixels have a frequencyresponse level greater than or equal to the threshold frequency responselevel, a step of authentication or identification of an individual bycomparison of fingerprints on the basis of the modified image.
 2. Methodaccording to claim 1, in which the allocation, to a pixel, of afrequency response level by evaluating the variabilities of the greylevels of the pixels contained in a window of determined size positionedaround each pixel comprises the determination of a gradient of greylevels, the frequency response level allocated to said pixel being basedon said gradient of grey levels.
 3. Method according to claim 1, inwhich the regions of the image defined by grouping together neighbouringpixels of same frequency response level are structured, to determine thethreshold frequency response level, into a topological tree or aconnected component tree, defining a parent relationship between saidregions of the image.
 4. Method according to claim 1, in which theregions of the image are structured according to a parent-daughterrelationship defined as follows, for each region i of the image:initially all the neighbouring regions of the region i on the image areconsidered as potential parents, if the number of potential parents isequal to 1, the neighbour j is assigned as parent to the region i, andthe region i is removed from the list of potential parents of the regionj, if not, each parent region of the region i is selected from the listof potential parents having a frequency response level lower than thatof the region i, as that having the level the closest to that of theregion i, and the region i is removed from the list of potential parentsof its parent region, and if all the potential parents of the region ihave a frequency response level greater than that of the region i, eachparent region of the region i is determined as that having the frequencyresponse level the closest to that of the region i and the region i isremoved from the list of potential parents of its parent region. 5.Method according to claim 3, in which the determination of the thresholdfrequency response level comprises the implementation of the followingsteps: for a set frequency response level N, definition of a set ofmacro-regions designated level N, such that each macro-region of level Ncomprises a parent region of pixels of frequency response level lowerthan or equal to N and the set of daughter regions of this region, foreach value of frequency response level from an initial value offrequency response level in the image, calculation of the relativevariation in surface of the macro-regions of a level N_(n) compared tothe macro-regions of the preceding level N_(n-1), and the minimumfrequency response level is determined as the level N_(i) for which therelative variation in surface of the macro-regions of level N_(i)compared to the preceding level N_(i-1) is minimal.
 6. Method accordingto claim 1, in which the step of segmentation comprises, before thedefinition of the regions of the image by grouping together neighbouringpixels of same frequency response level, the implementation of the stepsof: morphological erosion then dilation of the image as a function ofthe values of grey level of the pixels, generation of a differentialimage by subtraction, from the initial image, of the image havingundergone morphological erosion and dilation, application, to thedifferential image, of a median filter on the value of the frequencyresponse level, and in which the definition of the regions of the imageby grouping together neighbouring pixels of same frequency responselevel is implemented on the image resulting from the application of themedian filter on the differential image.
 7. Method according to claim 1,further comprising, after the step of segmentation, a step ofenhancement of the grey levels associated with the pixels of themodified image, the degree of enhancement of the pixels of a regionbeing a function of the frequency response level of the pixels of theregion.
 8. Method according to claim 7, in which the higher thefrequency response level of the pixels of a region of the modifiedimage, the higher the degree of enhancement of grey levels associatedwith the pixels of the region.
 9. Computer programme product, comprisinga computer readable support with a memory storing code instructions forthe implementation of the method according to claim 1, when instructionsare executed by a computer.
 10. Image processing system, comprising aprocessing unit of the processing means suited to implementing themethod according to claim
 1. 11. Image processing system according toclaim 10, further comprising an image acquisition means, said imageacquisition means being a fingerprint sensor of thin film transistortype.